Clinical and radio-angiographic features of paediatric moyamoya disease in Bangladesh

Background: Moyamoya disease is a cerebrovascular arteriopathy of unknown origin characterized by progressive stenosis followed by occlusion of the cerebral arteries. Studies on moyamoya disease, especially in children in Bangladesh, are rare. We aimed to determine the clinical and neuroimaging features of moyamoya disease, particularly angiographic features. Methods: Forty children diagnosed with moyamoya disease were consecutively recruited from Bangabandhu Sheikh Mujib Medical University Hospital, Dhaka, Bangladesh. Each patient underwent a medical history and physical examination focusing on stroke, magnetic resonance imaging, and magnetic resonance angiography scans of the brain. In some instances, electroencephalogram and digital subtraction angiography were also performed. Results: Of the 40 patients, 22 experienced their first-ever stroke (median age, 84 months), and 18 had recurrent strokes (median age, 90 months). Common symptoms included hemiparesis, headache, seizure, and speech disorder. The commonly affected vessels were the internal carotid and middle cerebral arteries. Cortical involvement was found in 82.5% of cases. Bilateral involvement was observed in 37.5% of the patients, most of whom were in the Suzuki stage III. Conclusion: Hemiparesis, headache, seizure, and speech disorder were the common manifestations. Most patients reported late (Suzuki stages III and IV), indicating an advanced stage.  Early detection is necessary, considering the severity of the disease and its inherent tendency for recurrence.  

Predicting the success of suicide terrorist attacks using different machine learning algorithms

Extremism has become one of the major threats throughout the world over the past few decades. In the last two decades, there has been a sharp increase in extremism and terrorist attacks. Nowadays, terrorism concerns all nations in terms of national security and is considered one of the most priority research topics. In order to support the national defense system, academics and researchers are analyzing various datasets to determine the reasons behind these attacks, their patterns, and how to predict their success. The main objective of our paper is to predict different types of attacks, such as successful suicide attacks, successful non-suicide attacks, unsuccessful suicide attacks, and unsuccessful non-suicide attacks. For this purpose, various machine learning algorithms, namely Random Forest, K Nearest Neighbor, Decision Tree, LightGBM Boosting, and a feedforward Artificial Neural Network called Multilayer Perceptron (MLP), are used to determine the success of suicide terrorist attacks. With an accuracy rate of 98.4% and an AUC-ROC score of 99.9%, the Random Forest classifier was the most accurate among all other algorithms. This model is more trustworthy than previous work and provides a useful comparison between machine learning methods and an artificial neural network because it is less dependent and has a multiclass target feature

A Novel Non-Invasive Estimation of Respiration Rate from Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model

Respiratory ailments such as asthma, chronic obstructive pulmonary disease (COPD), pneumonia, and lung cancer are life-Threatening. Respiration rate (RR) is a vital indicator of the wellness of a patient. Continuous monitoring of RR can provide early indication and thereby save lives. However, a real-Time continuous RR monitoring facility is only available at the intensive care unit (ICU) due to the size and cost of the equipment. Recent researches have proposed Photoplethysmogram (PPG) and/ Electrocardiogram (ECG) signals for RR estimation however, the usage of ECG is limited due to the unavailability of it in wearable devices. Due to the advent of wearable smartwatches with built-in PPG sensors, it is now being considered for continuous monitoring of RR. This paper describes a novel approach for RR estimation using motion artifact correction and machine learning (ML) models with the PPG signal features. Feature selection algorithms were used to reduce computational complexity and the chance of overfitting. The best ML model and the best feature selection algorithm combination were fine-Tuned to optimize its performance using hyperparameter optimization. Gaussian Process Regression (GPR) with Fit a Gaussian process regression model (Fitrgp) feature selection algorithm outperformed all other combinations and exhibits a root mean squared error (RMSE), mean absolute error (MAE), and two-standard deviation (2SD) of 2.63, 1.97, and 5.25 breaths per minute, respectively. Patients would be able to track RR at a lower cost and with less inconvenience if RR can be extracted efficiently and reliably from the PPG signal. 2013 IEEE.Corresponding authors: Muhammad E. H. Chowdhury ([email protected]), Mamun Bin Ibne Reaz ([email protected]), and Md. Shafayet Hossain ([email protected]) This work was supported in part by the Qatar National Research under Grant NPRP12S-0227-190164, and in part by the International Research Collaboration Co-Fund (IRCC) through Qatar University under Grant IRCC-2021-001. The statements made herein are solely the responsibility of the authors.Scopu

Socioeconomic Status and Prevalence of Obesity and Diabetes in a Mexican American Community, Cameron County, Texas, 2004-2007

Rates of obesity and diabetes in this border community are among the highest in the United States. Belonging to the lower socioeconomic stratum significantly increased the likelihood of having undiagnosed diabetes and, in patients too young to be eligible for Medicare, the overall risk of developing diabetes. Modest improvement in income has a beneficial effect on health in this racial/ethnic minority community

Grey, blue, and green hydrogen: A comprehensive review of production methods and prospects for zero-emission energy

Energy is the linchpin for economic development despite its generation deficit worldwide. Hydrogen can be used as an alternative energy source to meet the requirement that it emits zero to near-zero impurities and is safe for the environment and humans. Because of growing greenhouse gas emissions and the fast-expanding usage of renewable energy sources in power production in recent years, interest in hydrogen is resurging. Hydrogen may be utilized as a renewable energy storage, stabilizing the entire power system and assisting in the decarbonization of the power system, particularly in the industrial and transportation sectors. The main goal of this study is to describe several methods of producing hydrogen based on the principal energy sources utilized. Moreover, the financial and ecological outcomes of three key hydrogen colors (gray, blue, and green) are discussed. Hydrogen’s future prosperity is heavily reliant on technology advancement and cost reductions, along with future objectives and related legislation. This research might be improved by developing new hydrogen production methods, novel hydrogen storage systems, infrastructure, and carbon-free hydrogen generation

Availability and price changes of potential medicines and equipment for the prevention and treatment of COVID-19 among pharmacy and drug stores in Bangladesh; findings and implications

Objective: There are concerns with increased prices and drug shortages for pertinent medicines and personal protective equipment (PPE) to prevent and treat COVID-19 enhanced by misinformation. Community pharmacists and drug stores play a significant role in disease management in Bangladesh due to high co-payments. Consequently, a need to review prices and availability in the pandemic. Materials and Methods: Multiple approach involving a review and questionnaire among pharmacies and stores early March to end May 2020. Results and Discussion: 170 pharmacies and drug stores took part, giving a response rate of 63.9%. Encouragingly, no change in utilization of antimalarial medicines in 51.2% of stores despite global endorsements. However, increased utilisation of antibiotics (70.6%), analgesics (97.6%), vitamins (90.6%) and PPE (over 95%). Encouragingly, increases in purchasing of PPE. No increase in prices among 50% of the stores for antimalarials, with a similar situation for antibiotics (65.3%), analgesics (54.7%), and vitamins (51.8%). However, price increases typically for PPE (over 90% of stores). Shortages also seen for medicines and PPE, again greater for PPE. Conclusions: The pandemic has impacted on the supply and prices of medicines and PPE in Bangladesh. Key stakeholder groups can play a role addressing misinformation, with enhanced local production helping address future shortages and prices

Healthcare Facilities as Potential Reservoirs of Antimicrobial Resistant Klebsiella pneumoniae:An Emerging Concern to Public Health in Bangladesh

The emergence of virulent extended spectrum β-lactamase producing Klebsiella pneumoniae (ESBL-KP) including carbapenem-resistant Klebsiella pneumoniae (CRKP) in hospital-acquired infections has resulted in significant morbidity and mortality worldwide. We investigated the antibiotic resistance and virulence factors associated with ESBL-KP and CRKP in tertiary care hospitals in Bangladesh and explored their ability to form biofilm. A total of 67 ESBL-KP were isolated from 285 Klebsiella pneumoniae isolates from environmental and patient samples from January 2019 to April 2019. For ESBL-KP isolates, molecular typing was carried out using enterobacterial repetitive intergenic consensus polymerase chain reaction (ERIC-PCR), antibiotic susceptibility testing, PCR for virulence and drug-resistant genes, and biofilm assays were also performed. All 67 isolates were multidrug-resistant (MDR) to different antibiotics at high levels and 42 isolates were also carbapenem-resistant. The most common β-lactam resistance gene was bla(CTX-M-1) (91%), followed by bla(TEM) (76.1%), bla(SHV) (68.7%), bla(OXA-1) (29.9%), bla(GES) (14.9%), bla(CTX-M-9) (11.9%), and bla(CTX-M-2) (4.5%). The carbapenemase genes bla(KPC) (55.2%), bla(IMP) (28.4%), bla(VIM) (14.9%), bla(NDM-1) (13.4%), and bla(OXA-48) (10.4%) and virulence-associated genes such as fimH (71.6%), ugeF (58.2%), wabG (56.7%), ureA (47.8%) and kfuBC (28.4%) were also detected. About 96.2% of the environmental and 100% of the patient isolates were able to form biofilms. ERIC-PCR-based genotyping and hierarchical clustering of K. pneumoniae isolates revealed an association between environmental and patient samples, indicating clonal association with possible transmission of antimicrobial resistance genes. Our findings can help in improving patient care and infection control, and the development of public health policies related to hospital-acquired infections